What is In-memory Analytics?

In-memory analytics is used to query data in random access memory (RAM) instead of data stored on physical disks. This represents a huge shift in how organizations use data to tackle business challenges. As data remains in the RAM of a set of computers, many users can share it across multiple applications concurrently, quickly and securely. 

What is driving in-memory adoption?

The mainstream adoption of 644-bit architectures offers more addressable memory space and a fall in the price of memory. The rapidly changing infrastructure landscape has made it realistic to analyze large data sets entirely in-memory. 

Another factor driving in-memory adoption is growing volumes of data and tighter regulations introduced to maintain data and keep it available for years. Much data still resides on legacy systems which are expensive to run and maintain. To be able to access data on an ad-hoc basis for analytical purposes without having to build complex data warehouses is a significant driver for in-memory adoption.

Very little of the data accumulated is ever used by businesses for analytical purposes. When running analytical processes, they often have to run overnight so as not to cause system contention. Rather than processing data in a way that offers detailed reports, businesses often settle for aggregate reports that don’t offer the type of information that supports the best business decisions. 

One of the advantages of in-memory computing is that multiple users can access and analyze data quickly and securely. The in-memory approach provides the ability to analyze large data sets speedily and an in-memory database is much simpler to set up and administer than a data warehouse. IT is not burdened with all the time-consuming performance tuning tasks they are typically required with a data warehouse. 

The data analytics process is simplified by in-memory computing due to a reduction in layers. A team can create more efficient, simpler models and test, modify and deploy them in less time. Rapid adaptation of analytical models to shifting needs is possible and the plugging in of new data sources as additional sources of information is easy. 

What is in-memory analytics?

When a user runs a query that goes to a typical data warehouse, information is read from tables stored on a hard disk. With an in-memory database, all information is initially loaded into memory, where users can query and interact with it. Since data is kept in-memory, the response time of any calculation is very fast, even on extremely large data sets analyzed by many concurrent users. 

Analytics involves doing comparisons at an organizational level and fast performance depends on how quick it is to access each data record and perform a computation. Traditional analytical tools are no longer adequate when it comes to analyzing increasing data volumes at a speed sufficient to make timely decisions. 

When data records are available in-memory, this revolutionizes analytics because there is no need to fetch data from disks. Ultimately, businesses are using in-memory computing to remove latencies in the analytical lifecycle that comes from using disks. 

The business value of in-memory analytics

By using in-memory analytics, businesses can gain the insights they need to manage risks, improve customer satisfaction and much more. It has the speed and scaling advantages to handle analytics workloads and offer deep insights. 

Users are able to gather data, initiate queries, create visualizations, analyze and filter information on their own. They can see and understand a business in new ways and engage with data at blazing speed, resulting in more informed, proactive decision-making. They now have access to the right kind of information without any delay to deliver the deep insights they need to optimize business performance.

A common approach to speeding up query performance is caching. In-memory databases don’t have the same limitations as caching. The key difference is that cached data is usually predefined and specific whereas an in-memory database contains data available for analysis that is potentially as large as a whole data mart. 

When businesses need to develop a full data mart, the cost of development and storage is significant but in-memory analysis makes accessing and analyzing large data volumes possible at a low cost. 

An analytics suite can grow and expand as data expands. Most cloud solutions allow businesses the type of scalability to upgrade or downgrade seamlessly depending on business needs. 

By utilizing self-service business intelligence apps, users can benefit from data in ways that make business processes and customer service more efficient, which helps to increase profits. In cloud application development, developers must be mindful of and plan for time delays as data flows between networking devices and web applications.

In-memory analytics offers the flexibility to create custom reports for different user groups on-demand and teams can instantly receive the insights they need at a specific time. Everyone in the business also has deeper visibility into business-wide processes. 

Who is in-memory analytics for?

In-memory analytics is ideal for meeting the business intelligence needs of small to medium-sized businesses. It doesn’t need much up-front effort or ETL and an in-memory database can be quickly populated from any database source to use for analysis. There are minimal constraints when it comes to analytical scope, data scalability and drill-through capacity. 

A centralized data warehouse has to accommodate many diverse needs and this can translate into long wait times to produce reports. In-memory analytics is a great alternative to this because users can analyze vast quantities of data in-memory and get quick answers. 

Including IT early on when introducing in-memory analytics can result in a flexible, scalable platform. In-memory analytics allows for more self-service capabilities for end users and less dependence on IT but IT has to be careful that in-memory analytics is part of a comprehensive information architecture and not a stand-alone strategy. It doesn’t help to speed up data preparation and analytics unless downstream decision-makers can effectively utilize the business insights.

Conclusion

In-memory computing and analytics can help a business gain a new competitive edge by unlocking access to valuable, real-time insights and enabling speedy analytics on huge amounts of data.